{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b955d18-68ec-4b54-a1e3-5f34de2cb6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备工具\n",
    "# 需要安装依赖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6509b9d-4869-47c0-b630-c4147d7ff847",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tiktoken in d:\\software\\anaconda3\\lib\\site-packages (0.7.0)\n",
      "Requirement already satisfied: openai in d:\\software\\anaconda3\\lib\\site-packages (1.41.0)\n",
      "Requirement already satisfied: pandas in d:\\software\\anaconda3\\lib\\site-packages (2.1.4)\n",
      "Requirement already satisfied: matplotlib in d:\\software\\anaconda3\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: plotly in d:\\software\\anaconda3\\lib\\site-packages (5.9.0)\n",
      "Requirement already satisfied: scikit-learn in d:\\software\\anaconda3\\lib\\site-packages (1.2.2)\n",
      "Requirement already satisfied: numpy in d:\\software\\anaconda3\\lib\\site-packages (1.26.4)\n",
      "Requirement already satisfied: regex>=2022.1.18 in d:\\software\\anaconda3\\lib\\site-packages (from tiktoken) (2023.10.3)\n",
      "Requirement already satisfied: requests>=2.26.0 in d:\\software\\anaconda3\\lib\\site-packages (from tiktoken) (2.31.0)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (4.2.0)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (1.8.0)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (0.27.0)\n",
      "Requirement already satisfied: jiter<1,>=0.4.0 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (0.5.0)\n",
      "Requirement already satisfied: pydantic<3,>=1.9.0 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (1.10.12)\n",
      "Requirement already satisfied: sniffio in d:\\software\\anaconda3\\lib\\site-packages (from openai) (1.3.0)\n",
      "Requirement already satisfied: tqdm>4 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (4.65.0)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.11 in d:\\software\\anaconda3\\lib\\site-packages (from openai) (4.12.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\software\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\software\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in d:\\software\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\software\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in d:\\software\\anaconda3\\lib\\site-packages (from plotly) (8.2.2)\n",
      "Requirement already satisfied: scipy>=1.3.2 in d:\\software\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in d:\\software\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\software\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: idna>=2.8 in d:\\software\\anaconda3\\lib\\site-packages (from anyio<5,>=3.5.0->openai) (3.4)\n",
      "Requirement already satisfied: certifi in d:\\software\\anaconda3\\lib\\site-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
      "Requirement already satisfied: httpcore==1.* in d:\\software\\anaconda3\\lib\\site-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in d:\\software\\anaconda3\\lib\\site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
      "Requirement already satisfied: six>=1.5 in d:\\software\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\software\\anaconda3\\lib\\site-packages (from requests>=2.26.0->tiktoken) (2.0.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\software\\anaconda3\\lib\\site-packages (from requests>=2.26.0->tiktoken) (2.0.7)\n",
      "Requirement already satisfied: colorama in d:\\software\\anaconda3\\lib\\site-packages (from tqdm>4->openai) (0.4.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install tiktoken openai pandas matplotlib plotly scikit-learn numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fe8f62d-a6d0-49f5-98d4-eabf8a5fc1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导包\n",
    "import pandas as pd\n",
    "# 导入 tiktoken 库。Tiktoken 是 OpenAI 开发的一个库，用于从模型生成的文本中计算 token 数量。\n",
    "import tiktoken"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f30a8d-063d-4b67-8cb2-c9830cec5c8c",
   "metadata": {},
   "source": [
    "# 加载数据集\n",
    "以下是从亚马逊公公的美食评论中，挑选了1000条数据，用来做embedding测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b40e20a-35d3-49a5-b6e0-acf028d96827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003JK537S</td>\n",
       "      <td>A3JBPC3WFUT5ZP</td>\n",
       "      <td>1</td>\n",
       "      <td>Arrived in pieces</td>\n",
       "      <td>Not pleased at all. When I opened the box, mos...</td>\n",
       "      <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time   ProductId          UserId  Score  \\\n",
       "0  1351123200  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "1  1351123200  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  where does one  start...and stop... with a tre...   \n",
       "1                                  Arrived in pieces   \n",
       "\n",
       "                                                Text  \\\n",
       "0  Wanted to save some to bring to my Chicago fam...   \n",
       "1  Not pleased at all. When I opened the box, mos...   \n",
       "\n",
       "                                            combined  \n",
       "0  Title: where does one  start...and stop... wit...  \n",
       "1  Title: Arrived in pieces; Content: Not pleased...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_datapath = \"data/fine_food_reviews_1k.csv\"\n",
    "# 读取文件，index_col=0的意思是指将第0行，作为文件的索引\n",
    "df = pd.read_csv(input_datapath , index_col=0)\n",
    "# 这一行代码，是选中需要的列，就是比如说一共有10列，那么这里指定完之后，其它的列就会被删除掉\n",
    "df = df[['Time' , 'ProductId' , 'UserId' , 'Score' , 'Summary' , 'Text']]\n",
    "# 如果某一行中，某个字段的值为空，即Nan，则整行都会被删除掉\n",
    "df = df.dropna()\n",
    "\n",
    "# 将 Summary 和 Text 组合成一个新的字段 Combined\n",
    "df['combined'] = (\"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip())\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79645c52-cfad-4f5a-a885-d2c26625bf96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Title: where does one  start...and stop... wit...\n",
       "1      Title: Arrived in pieces; Content: Not pleased...\n",
       "2      Title: It isn't blanc mange, but isn't bad . ....\n",
       "3      Title: These also have SALT and it's not sea s...\n",
       "4      Title: Happy with the product; Content: My dog...\n",
       "                             ...                        \n",
       "995    Title: Delicious!; Content: I have ordered the...\n",
       "996    Title: Good Training Treat; Content: My dog wi...\n",
       "997    Title: Jamica Me Crazy Coffee; Content: Wolfga...\n",
       "998    Title: Party Peanuts; Content: Great product f...\n",
       "999    Title: I love Maui Coffee!; Content: My first ...\n",
       "Name: combined, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"combined\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0af37a-e08a-4b32-a0d9-013c13bcd45b",
   "metadata": {},
   "source": [
    "# Embedding 模型关键参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e342b92-bcd3-43cf-a48b-014b60794f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型类型\n",
    "# 这里使用了 text-embedding-ada-002 模型，模型列表可以在这里查看：https://platform.openai.com/docs/models/embeddings\n",
    "embedding_model = \"text-embedding-ada-002\"\n",
    "# text-embedding-ada-002 对应的分词器 (TOKENIZER)，openai老的文档中有，但是新的文档中没有找到这个介绍了\n",
    "embedding_encoding = \"cl100k_base\"\n",
    "# text-embedding-ada-002模型支持的最大输入Token是9191，向量维度1536\n",
    "# 所以在Demo中，过滤掉Token数量大于8000的文本\n",
    "max_tokens = 8000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0017bc9-bb4c-4aa2-9ec6-7c569c799b01",
   "metadata": {},
   "source": [
    "# 将文本减少到最近的1000个评论，并删除掉过长的样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f13a472b-5280-4e17-9635-ffb0a0f8f565",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置要需要的评论数量为1000\n",
    "top_n = 1000\n",
    "# 对于 DataFrame 进行排序，基于Time列，然后选取最后的2000条评论(实际上表格中只有1000条数据)\n",
    "# 这个假设是，最近的评论会更加有效\n",
    "df = df.sort_values(\"Time\").tail(top_n * 2)\n",
    "# 删除掉 Time 这一列，因为后面用不到了\n",
    "# 删除掉time这一列，这里有三个参数，参数一：列名， 参数二：删除第1列，参数三：不再生成一个新的DataFrame，而是替换原有的\n",
    "# 这里必须指定 axis ， 因为df.drop(\"Time\") 默认是按行来删除，而不是按列，这就会报错，因为这个DataFrame中，索引是列，而不是行\n",
    "# 因此，为了确保你是在删除列而不是行，必须显式地设置axis=1。这样，pandas就知道你的意图是删除列而不是行。\n",
    "df.drop(\"Time\" , axis = 1 , inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "281ba655-f0aa-4fce-aa1b-e7125d090c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从embedding_encoding 获取编码\n",
    "encoding = tiktoken.get_encoding(embedding_encoding)\n",
    "# 计算每条token的数量，通过 encoding.encode方法获取每条评论的token数量，然后把结果存储在新的n_tokens列中\n",
    "df[\"n_tokens\"] = df.combined.apply(lambda x: len(encoding.encode(x)))\n",
    "\n",
    "# 查看是否有token数量超过了最大允许的token数量，如果有的话，就删除掉\n",
    "df = df[df.n_tokens <= max_tokens].tail(top_n)\n",
    "# 打印出剩余的评论数量\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c22ce85-9fb9-4d5b-94e3-2e4ff849367e",
   "metadata": {},
   "source": [
    "# 生成Embeddings并且保存\n",
    "> 这里会耗费几分钟的时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdf92947-d265-4bf9-8002-d90602e0f235",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d88bde06-a2fd-416b-aa02-1d1deb1eeca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenAI Python SDK v1.0 更新后的使用方式\n",
    "# 这里要改一下URL，没法直接调用 openai，买了淘宝的代理，  https://help.apikeyplus.com/doc/3/\n",
    "client = OpenAI(base_url=\"https://apikeyplus.com/v1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82a41bfe-ee8b-4021-bff5-02c91597c79e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.011040176264941692, -0.01456454023718834, -0.02927062101662159, -0.004490379244089127, 0.014465461485087872, 0.030318021774291992, 0.007890895009040833, 0.006730261258780956, 0.014536231756210327, -0.006450718268752098, 0.008478289470076561, -0.006907186936587095, 0.0017789899138733745, -0.027940137311816216, 0.0324128232896328, 0.0014357536565512419, 0.025236710906028748, 0.004610688891261816, 0.01559778768569231, 0.006822262890636921, 0.01755104959011078, 0.017267968505620956, -0.013262365013360977, -0.009108145721256733, -0.012207887135446072, 0.00973800104111433, -0.029242312535643578, -0.018230443820357323, -0.004784076474606991, -0.02566133253276348, -0.01933446153998375, 0.012186655774712563, 0.026892736554145813, -0.013934683986008167, 0.04976571723818779, 0.01667349599301815, -0.01139402762055397, 0.027359820902347565, -0.0018612605053931475, 0.022943750023841858, 0.017607664689421654, 0.007494580931961536, -0.02290128916501999, 0.0013287134934216738, 0.0223492793738842, 0.013545447029173374, -0.008499519899487495, -0.019645851105451584, -0.008442903868854046, -0.015880867838859558, -0.006397640332579613, 0.012314043007791042, -0.020070474594831467, 0.0006289715529419482, -0.0033438995014876127, 0.0111746396869421, 0.018952302634716034, 0.013198671862483025, -0.013715296052396297, -0.005035310983657837, 0.006068558432161808, 0.011266641318798065, -0.011054330505430698, -0.01576763577759266, 0.005049465224146843, 0.005838554818183184, -0.018768299371004105, -0.04444378614425659, 0.022816363722085953, -0.015427938662469387, 0.0005688167875632644, -0.0047239214181900024, 0.0036446736194193363, 0.02100464329123497, -0.00121990405023098, 0.01565440371632576, -0.027982600033283234, -0.02336837351322174, -0.002098341239616275, -0.00603317329660058, -0.0224625114351511, -0.010113084688782692, -0.015682712197303772]\n"
     ]
    }
   ],
   "source": [
    "# 新版本创建 Embedding 向量的方法\n",
    "# Ref：https://community.openai.com/t/embeddings-api-documentation-needs-to-updated/475663\n",
    "# 这里测试abc这个值的 embedding 结果\n",
    "res = client.embeddings.create(input=\"abc\", model=embedding_model)\n",
    "print(res.data[0].embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b7d21d-570c-4425-af6e-53a76af5eadd",
   "metadata": {},
   "source": [
    "# 定义一个方法，来获取embedding 的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "255cfd94-facf-4f67-b589-0df75fc16de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用新方法调用 OpenAI Embedding API\n",
    "def embedding_text(text, model=\"text-embedding-ada-002\"):\n",
    "    res = client.embeddings.create(input=text, model=model)\n",
    "    return res.data[0].embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "850c75d9-a8a6-4aa9-ab41-5bd1f0c81ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok...\n"
     ]
    }
   ],
   "source": [
    "# 实际生成会耗时几分钟，逐行调用 OpenAI Embedding API\n",
    "\n",
    "#df[\"embedding\"] = df.combined.apply(embedding_text)\n",
    "#output_datapath = \"data/fine_food_reviews_with_embeddings_1k_1126.csv\"\n",
    "#df.to_csv(output_datapath)\n",
    "print(\"ok...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc0eae2-1857-4194-8360-0245049fa9f5",
   "metadata": {},
   "source": [
    "# 正式开始（正常代码执行，可以从这里开始）\n",
    "上面的代码，是初始化数据，调用embedding，上面的执行结果保存起来了，下面开始，就是直接使用这些数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "859144f3-70d9-43bf-abd3-e42e86c7af05",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_datapath = \"data/fine_food_reviews_with_embeddings_1k_1126.csv\"\n",
    "\n",
    "df_embedded = pd.read_csv(embedding_datapath, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afb04e57-845f-4c1a-9d5f-2453efef5b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [0.006959947757422924, -0.027468593791127205, ...\n",
       "297    [-0.0031723494175821543, -0.010055126622319221...\n",
       "296    [-0.017520075663924217, -0.0001107390780816786...\n",
       "295    [-0.0013888817047700286, -0.011071442626416683...\n",
       "294    [-0.017520075663924217, -0.0001107390780816786...\n",
       "                             ...                        \n",
       "623    [0.0002511601778678596, -0.005049135070294142,...\n",
       "624    [-0.020889893174171448, -0.013193264603614807,...\n",
       "625    [-0.009794735349714756, -0.006853016559034586,...\n",
       "619    [-0.005226599983870983, 0.0009393842192366719,...\n",
       "999    [-0.00609060050919652, -0.015060395002365112, ...\n",
       "Name: embedding, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"embedding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d017137-bf23-4f03-8fe7-9ade812fb319",
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv 表格中，存储的是字符串，类似json数组的字符串形式\n",
    "# 将字符串，转换为向量\n",
    "\n",
    "import ast\n",
    "df_embedded[\"embedding_vec\"] = df_embedded[\"embedding\"].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "54281d1b-a768-416b-91eb-8b3aa4e54b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_embedded[\"embedding_vec\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a5caecfa-bb9f-4507-82c4-c5b2d7768e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embeddint_vec</th>\n",
       "      <th>embedding_vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "      <td>178</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId          UserId  Score  \\\n",
       "0    B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "\n",
       "                                               Summary  \\\n",
       "0    where does one  start...and stop... with a tre...   \n",
       "297                   Good, but not Wolfgang Puck good   \n",
       "\n",
       "                                                  Text  \\\n",
       "0    Wanted to save some to bring to my Chicago fam...   \n",
       "297  Honestly, I have to admit that I expected a li...   \n",
       "\n",
       "                                              combined  n_tokens  \\\n",
       "0    Title: where does one  start...and stop... wit...        52   \n",
       "297  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "\n",
       "                                             embedding  \\\n",
       "0    [0.006959947757422924, -0.027468593791127205, ...   \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...   \n",
       "\n",
       "                                         embeddint_vec  \\\n",
       "0    [0.006959947757422924, -0.027468593791127205, ...   \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...   \n",
       "\n",
       "                                         embedding_vec  \n",
       "0    [0.006959947757422924, -0.027468593791127205, ...  \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110529f4-c98c-4c78-98a6-0b9abe8b4763",
   "metadata": {},
   "source": [
    "# 使用T-SNE 可视化 1536 维的 Embedding 的美食评论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb300f43-9218-4aad-a6e1-0040ee7f0198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 numpy 包\n",
    "import numpy as np\n",
    "# 这是一个汇图的框架\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# 从sklearn.manifold 模块中导入TSNE 类\n",
    "# TSNE (t-Distributed Stochastic Neighbor Embedding) 是一种用于数据可视化的降维方法，尤其擅长处理高维数据的可视化。\n",
    "# 它可以将高维度的数据映射到 2D 或 3D 的空间中，以便我们可以直观地观察和理解数据的结构。\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ae6b4943-6ef9-4cba-8d85-c4391b457985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding_vec\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d89155b-f94d-4fda-ae2b-dc741c57c4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先，需要确保嵌入的向量，都是等长的\n",
    "# 这里的代码是，使用 embedding_vec 这一列，取个值的长度，然后去除，去重后，如果只有一个值，就代表着长度是相等的\n",
    "assert df_embedded[\"embedding_vec\"].apply(len).nunique() == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "88bc5767-d71e-4e2c-860c-b2c03cfe5db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00695995, -0.02746859,  0.01054475, ..., -0.00712149,\n",
       "        -0.02196937, -0.03755966],\n",
       "       [-0.00317235, -0.01005513, -0.0036378 , ..., -0.00986366,\n",
       "        -0.00253524, -0.00911101],\n",
       "       [-0.01752008, -0.00011074, -0.01151435, ..., -0.01386558,\n",
       "        -0.03899023, -0.02353924],\n",
       "       ...,\n",
       "       [-0.00979474, -0.00685302, -0.00581748, ..., -0.03010315,\n",
       "        -0.008126  , -0.01941798],\n",
       "       [-0.0052266 ,  0.00093938,  0.02825824, ..., -0.00540942,\n",
       "        -0.01335868, -0.02726581],\n",
       "       [-0.0060906 , -0.0150604 , -0.00214962, ..., -0.0291046 ,\n",
       "        -0.01416146, -0.02285115]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将向量列表转换为二维的 numpy 数组\n",
    "matrix = np.vstack(df_embedded[\"embedding_vec\"].values)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34a2cc49-6a92-4fe2-8a42-3dc53e29f47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个 t-SNE 模型，t-SNE 是一种非线性降维方法，常用于高维数据的可视化。\n",
    "# n_components 表示降维后的维度（这里的值是2，代表着在一个平面上面显示）\n",
    "# perplexity 可以被理解为近邻的数量（是t-SNE中一个非常重要的参数，它控制了在数据中局部邻域的大小。较高的perplexity值意味着考虑更多的近邻点。该值通常根据数据集的大小来调整，范围通常在5到50之间）\n",
    "# random_state 是随机数生成器的种子（是随机数生成器的种子，用于初始化t-SNE算法。设置这个参数可以保证实验的可重复性，即每次运行代码时都会得到相同的结果）\n",
    "# init 设置初始化方式\n",
    "# learning_rate 是学习率（参数控制了梯度下降过程中的步长大小。这个参数对t-SNE的性能影响很大，因为它决定了算法在优化过程中如何快速地移动点。如果学习率太高，算法可能会在最小值附近振荡；如果太低，则收敛可能会非常慢。）\n",
    "tsne = TSNE(n_components=2 , perplexity=15 , random_state=42 , init=\"random\" , learning_rate=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "695c953c-ff21-4cbf-9b85-79cbd2ba655e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 t-SNE 对数据进行降维，得到每个数据点在新的2D空间中的坐标\n",
    "# 最终会得到降维后的结果，将高维数组，降到了低维\n",
    "vis_dims = tsne.fit_transform(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8148e9e6-484c-4797-96a2-7f8c983b62be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义五种不同的颜色，用于可视化中表示不同的等级\n",
    "colors = [\"red\", \"darkorange\", \"gold\", \"turquoise\", \"darkgreen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "033cffc9-0f9e-40ba-b340-bb7323a306bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从降维后的坐标中，分别获取出所有数据点中的X坐标，和Y坐标\n",
    "x = [x for x,y in vis_dims]\n",
    "y = [y for x,y in vis_dims]\n",
    "\n",
    "# 根据数据点的评分，获取对应的颜色索引。（减1是因为评价是从1开始的，而颜色索引是从0开始的）\n",
    "colors_indices = df_embedded[\"Score\"].values - 1\n",
    "# 确保你的数据点，和颜色索引的数量匹配\n",
    "assert len(vis_dims) == len(df_embedded.Score.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "186519ad-1159-4cca-9579-22ba63798a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amazon ratings visualized in language using t-SNE')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "# 使用 matplotlib 创建散点图\n",
    "plt.scatter(x , y , c=colors_indices , cmap=colormap , alpha=0.3)\n",
    "\n",
    "#为图形添加标题\n",
    "plt.title(\"Amazon ratings visualized in language using t-SNE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a34bfa-863e-4278-9def-a90754ceeae2",
   "metadata": {},
   "source": [
    ">通过T-SNE 降维后，产生了约3个大类（这里是看散点，分布了3个区域），其中一个大类的评论大多是负面的（左上类）\n",
    "也就是评分，和评价的内容并没有直接的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c71b9e0-b22e-4519-b0ce-1b8df0308c8b",
   "metadata": {},
   "source": [
    "# 4：使用K-MEANS聚类，然后使用T-SNE可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e34faa63-03f2-4694-95a2-46aa18031bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# kmearns 是一个聚类算法\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# 聚类4个类型\n",
    "n_clusters = 4\n",
    "\n",
    "# 创建一个 KMeans 对象，用于进行 K-Means 聚类。\n",
    "# n_clusters 参数指定了要创建的聚类的数量；\n",
    "# init 参数指定了初始化方法（在这种情况下是 'k-means++' , 这是K均值算法的改进初始化方法，它试图选择初始中心点以加速收敛，而不是简单地从数据集中随机选择）；\n",
    "# random_state 参数为随机数生成器设定了种子值，用于生成初始聚类中心。\n",
    "# n_init(指定了算法运行的次数，并且每次运行都会使用不同的中心点初始化方法。最终的模型将是在这些运行中选择具有最小惯性（即聚类内部距离之和）的模型。默认值是10，意味着算法将随机初始化10次，然后选择最佳结果)\n",
    "# n_init=10 消除警告 'FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4'\n",
    "kmeans = KMeans(n_clusters=n_clusters , init=\"k-means++\" , random_state=42 , n_init=10)\n",
    "# 使用 matrix（我们之前创建的矩阵）来训练 KMeans 模型。这将执行 K-Means 聚类算法。\n",
    "kmeans.fit(matrix)\n",
    "\n",
    "# kmeans.labels_ 属性包含每个输入数据点所属的聚类的索引。\n",
    "# 这里，我们创建一个新的 'Cluster' 列，在这个列中，每个数据点都被赋予其所属的聚类的标签。\n",
    "df_embedded[\"Cluster\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fc85d765-b8b8-4621-ad23-64d698607246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "297    3\n",
       "296    2\n",
       "295    1\n",
       "294    2\n",
       "      ..\n",
       "623    3\n",
       "624    1\n",
       "625    0\n",
       "619    3\n",
       "999    3\n",
       "Name: Cluster, Length: 1000, dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"Cluster\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8da2051-30c2-4edf-b469-5e66b9de81ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embeddint_vec</th>\n",
       "      <th>embedding_vec</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "      <td>[0.006959947757422924, -0.027468593791127205, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "      <td>178</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "      <td>[-0.0031723494175821543, -0.010055126622319221...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId          UserId  Score  \\\n",
       "0    B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "\n",
       "                                               Summary  \\\n",
       "0    where does one  start...and stop... with a tre...   \n",
       "297                   Good, but not Wolfgang Puck good   \n",
       "\n",
       "                                                  Text  \\\n",
       "0    Wanted to save some to bring to my Chicago fam...   \n",
       "297  Honestly, I have to admit that I expected a li...   \n",
       "\n",
       "                                              combined  n_tokens  \\\n",
       "0    Title: where does one  start...and stop... wit...        52   \n",
       "297  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "\n",
       "                                             embedding  \\\n",
       "0    [0.006959947757422924, -0.027468593791127205, ...   \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...   \n",
       "\n",
       "                                         embeddint_vec  \\\n",
       "0    [0.006959947757422924, -0.027468593791127205, ...   \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...   \n",
       "\n",
       "                                         embedding_vec  Cluster  \n",
       "0    [0.006959947757422924, -0.027468593791127205, ...        2  \n",
       "297  [-0.0031723494175821543, -0.010055126622319221...        3  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "193a2add-fb4f-4fd0-86ca-19686a207bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Clustering visualized in 2D using t-SNE')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按聚类算法后的数据，显示出来\n",
    "colors = [\"red\", \"green\", \"blue\", \"purple\"]\n",
    "# 然后，你可以使用 t-SNE 来降维数据。这里，我们只考虑 'embedding_vec' 列。\n",
    "tsne_model = TSNE(n_components=2 , random_state=42)\n",
    "vis_data = tsne_model.fit_transform(matrix)\n",
    "#获取降维后的x坐标，和Y坐标\n",
    "x = vis_data[:,0]\n",
    "y = vis_data[:,1]\n",
    "# 使用Cluster列中的值，来关联颜色index\n",
    "color_indices = df_embedded[\"Cluster\"].values\n",
    "\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "plt.scatter(x , y , c=color_indices , cmap=colormap)\n",
    "plt.title(\"Clustering visualized in 2D using t-SNE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0deeb4d2-9230-4ddd-be15-c57f77c3c8a4",
   "metadata": {},
   "source": [
    "**这里的数据显示分类更加清楚了**\n",
    "\n",
    "通过查看数据可以发现分为4类：一个专注于狗粮，一个专注于负面评论，一个专注于正面的评论"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ccd169a-63ad-4412-9212-5f96c710579b",
   "metadata": {},
   "source": [
    "# 5：使用Embedding进行文本搜索。\n",
    "\n",
    "即使用向量空间的距离，查找最相似的评论，比如输入“很好吃的食物”，那么检索出最为相似的几条评论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b897ce66-9f19-40dd-96d9-2008e3e844a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cosine_similarity 函数计算两个嵌入向量之间的余弦相似度。\n",
    "def cosine_similarity(a, b):\n",
    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2194ee2d-30db-4343-87df-8db5e6ce3da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个名为 search_reviews 的函数，\n",
    "# Pandas DataFrame ，产品描述，查找的数量，以及一个 pprint 标志（默认值为 True）。\n",
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = embedding_text(product_description)\n",
    "    \n",
    "    df[\"similarity\"] = df.embedding_vec.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:200])\n",
    "            print()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "659a3fd4-a86c-4bb1-925c-2d4d77164ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "will do it again:  Service was great. Hot White Chocolate is awesome. I will be ordering again soon!! Even the family is hooked and wants some more to.\n",
      "\n",
      "Very pleased:  Good price, good quality with convenience.  Fully met my expectations.  Would recommend.  The breakfast blend was mild and a nice start to the morning.\n",
      "\n",
      "very good!!:  just like the runts<br />great flavor, def worth getting<br />I even ordered again 2 wks latter (all gone)<br />just like I remember :)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用 'delicious beans' 作为产品描述和 3 作为数量，\n",
    "# 调用 search_reviews 函数来查找与给定产品描述最相似的前3条评论。\n",
    "# 其结果被存储在 res 变量中。\n",
    "res = search_reviews(df_embedded, '非常好吃，下次还会去', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c5543cc3-41f0-4ac7-9e45-219dee04f767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plastic chew:  Puppy didn't care for this. Older dog chews on it. Not sure how much gets chewed up and digested. Not too crazy about this.\n",
      "\n",
      "Don't like the taste:  I do not like sour taste and this has a sour kind of taste which i don't like. The smell isn't that great either\n",
      "\n",
      "Disappointed:  The metal cover has severely disformed. And most of the cookies inside have been crushed into small pieces. Shopping experience is awful. I'll never buy it online again.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用 'delicious beans' 作为产品描述和 3 作为数量，\n",
    "# 调用 search_reviews 函数来查找与给定产品描述最相似的前3条评论。\n",
    "# 其结果被存储在 res 变量中。\n",
    "res = search_reviews(df_embedded, '已经投诉了，坏掉的东西不能吃', n=3)"
   ]
  }
 ],
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  "kernelspec": {
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